As the number of electronically available documents continues to increase (e.g., via storage in digital libraries, academic databases, research databases, online sources, etc.), the number of ambiguous authors (e.g., different authors with the same or similar name) becomes more prevalent. Accordingly, when a user is attempting to find documents published by a particular author via submitting an author-based search query, the user is often provided with unsatisfactory search results that include a plurality of documents from multiple different authors with the same or similar name. Thus, a search engine that performs author-based searches is commonly confused and inaccurate when attempting to locate a set of documents corresponding to the particular author.
For example, a student may be working on a school project, and therefore, may want to access and review all documents written by “Tom Jones”, a well-know professor at State University, whom the student is familiar with because he works in the same research area as the student's project. However, with the expanding global academic and professional human population, and the increasing availability of electronic documents available to search engines, there may be multiple different “Tom Jones” that have authored documents in the aforementioned research field or in other research fields. Thus, search engines (e.g., academic search engines) that provide author-based search functionality are presented with an author disambiguation problem that makes it difficult to provide accurate author-based search results that locate, match, and provide electronic documents for a particular author, while not providing electronic documents for other authors with the same or similar name that are not the intended focus of the user search.